- [Resource Hub](/) - [Publication](/?resource-type=publication#library) - SARS-CoV-2 infection protects against rechallenge in rhesus macaques # SARS-CoV-2 infection protects against rechallenge in rhesus macaques Chandrashekar A, Liu J, Martinot AJ, McMahan K, Mercado NB, Peter L, et al. Science . 2020;369(6505):812-817. DOI [10.1126/science.abc4776](https://doi.org/10.1126/science.abc4776). How to cite ### AMA Chandrashekar A, Liu J, Martinot AJ, McMahan K, Mercado NB, Peter L, et al. SARS-CoV-2 infection protects against rechallenge in rhesus macaques. Science . 2020;369(6505):812-817. doi:10.1126/science.abc4776 ### APA Chandrashekar, A., Liu, J., Martinot, A. J., McMahan, K., Mercado, N. B., Peter, L., et al. (2020). SARS-CoV-2 infection protects against rechallenge in rhesus macaques. Science , 369(6505), 812-817. https://doi.org/10.1126/science.abc4776 ### BibTeX @article{chandrashekar2020sarscov2, title = {SARS-CoV-2 infection protects against rechallenge in rhesus macaques}, author = {Chandrashekar, Abishek and Liu, Jinyan and Martinot, Amanda J. and McMahan, Katherine and Mercado, Noe B. and Peter, Lauren and others}, journal = {Science}, volume = {369}, number = {6505}, pages = {812--817}, year = {2020}, doi = {10.1126/science.abc4776} } Early in the COVID-19 pandemic one question mattered enormously and had no answer: once someone clears SARS-CoV-2, are they protected if they meet the virus again? There were no human data, yet the answer shapes vaccine strategy, public-health policy, and how we model herd immunity. To probe it, the team built a rhesus macaque model of infection. Nine animals were infected, allowed to recover, and then re-exposed to the same virus about five weeks later. On the second exposure they were almost completely protected: viral levels in the lower airway were more than five orders of magnitude lower than during the first infection, and no infectious virus could be recovered. Protection was not sterilizing. Instead the animals mounted a rapid recall immune response, and multiplex imaging of infected lung tissue showed exactly which cells the virus had occupied and which immune cells sat beside them. [Read publication at Science](https://doi.org/10.1126/science.abc4776) ## Key findings - Prior SARS-CoV-2 infection gave rhesus macaques near-complete protection against rechallenge. When all nine animals were re-exposed on day 35, median peak viral loads in bronchoalveolar lavage were >5.1 log10 lower than after the primary challenge (P ## CyteFinder in the methods “Slides were then subjected to CyCIF using the following antibodies (Table S1). Slides were scanned with a RareCyte CyteFinder scanner equipped with a 20x, 0.8 NA objective and images then corrected for uneven illumination using the BaSiC tool.” — Chandrashekar et al., Science (2020), Supplementary Materials, Methods (Cyclic immunofluorescence) ## Why it matters for CyteFinder users If you are weighing a RareCyte CyteFinder scanner for a tissue study, look at what its imaging arm was asked to carry here. The paper's spatial claim, that the virus occupied specific lung cells and that particular immune cells sat next to them, is a claim about position, and you cannot make it from a dissociated sample or a single hand-picked field of view. It needs single-cell locations across whole tissue, in enough channels to tell infected epithelium, several macrophage markers (Iba-1, CD68, CD206), T cells (CD3), B cells (CD20), neutrophils (MPO), and endothelium (CD31) apart at once. Here the authors ran cyclic immunofluorescence on formalin-fixed, paraffin-embedded rhesus lung with a multiplex antibody panel (Table S1) plus a DNA counterstain, and the CyteFinder captured each cycle at whole-slide scale through a 20x, 0.8 NA objective. Two things follow for your own workflow. Because the scan covers the entire section rather than a chosen frame, rare foci of infection are found in their true tissue context instead of being missed or cherry-picked. And because the instrument delivers high-resolution, multi-channel whole-slide images that downstream tools (here BaSiC illumination correction and ASHLAR registration) can align across cycles, you end up with an analysis-ready map on which the distance between one cell type and another can actually be measured, the spatial question a lower-plex or dissociated readout cannot answer. 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